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Record W2128542144

Galactic kinematics from RAVE to Gaia-RVS Data

2008· article· en· W2128542144 on OpenAlexafffund
L. Veltz, O. Bienaymé, Matthias Steinmetz, T. Zwitter, F. G. Watson, James Binney, Joss Bland‐Hawthorn, R. Campbell, K. C. Freeman, B. K. Gibson, G. Gilmore, E. K. Grebel, A. Helmi, U. Munari, Julio F. Navarro, Q. A. Parker, G. M. Seabroke, A. Siebert, A. Siviero, M. Williams, Rosemary F. Ġ. Wyse

Bibliographic record

VenueUniversity of Groningen research database (University of Groningen / Centre for Information Technology) · 2008
Typearticle
Languageen
FieldPhysics and Astronomy
TopicStellar, planetary, and galactic studies
Canadian institutionsUniversity of Victoria
FundersJavna Agencija za Raziskovalno Dejavnost RSNederlandse Organisatie voor Wetenschappelijk OnderzoekDeutsche ForschungsgemeinschaftNatural Sciences and Engineering Research Council of CanadaJohns Hopkins UniversitySchweizerischer Nationalfonds zur Förderung der Wissenschaftlichen ForschungIstituto Nazionale di AstrofisicaNational Science Foundation
KeywordsKinematicsAstrophysicsGalaxyPhysicsDiscScale (ratio)AstronomyGalaxy formation and evolutionClassical mechanics
DOInot available

Abstract

fetched live from OpenAlex

RAVE data has provided new results on Galactic kinematics like the kinematical decomposition of the Galactic disk.This decomposition permits to identify the different components of the disk and to characterize them in terms of scale height and scale length.With the data provided by Gaia and in particular the RVS, we will have a completly renewed view of the Galaxy.The precision of the RVS will permit to undertake a precise analysis of the kinematics of the Galactic disks.This knowledge will provide significant clues to constrain the scenarios of the Galactic disk formation.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.078
Threshold uncertainty score0.155

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0060.006
Science and technology studies0.0000.000
Scholarly communication0.0020.001
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0080.013

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.053
GPT teacher head0.261
Teacher spread0.208 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2008
Admission routes2
Has abstractyes

Explore more

Same venueUniversity of Groningen research database (University of Groningen / Centre for Information Technology)→Same topicStellar, planetary, and galactic studies→French-language works237,207→